Abstract
The analysis and identification of pathological signs associated with different respiratory diseases are is not an easy task. One of the imaging modalities for these signs identification is examining chest CT scans. However, it requires expert knowledge to avoid human error. The purpose of this work is to implement, test, and analyze the performance of a neural network based on a mask R-CNN model able to identify some pathological signs of respiratory disease. The CT images used were manually labeled and pre-classified as positive and negative cases by specialists to prepare them for the training process. Preliminary results reached detection of ground-glass opacity with a sensitivity of 81.89% using the validation set and 92.66% using the test set. Nevertheless, low percentages were obtained for pulmonary nodules detection with a sensitivity of 51.08 and 40.34% using validation and test sets, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Taleno DA (2017, September) Signos Radiológicos en Tomografía y Resonancia Magnética y Grado de Conocimiento de los Residentes de Radiología, en Pacientes atendidos en HEALF, Octubre 2013–2014,” Master’s thesis, Universidad Nacional Autónoma de Nicaragua, Managua, Nicaragua
Franquet T (2012) Diagnóstico por imagen de las enfermedades pulmonares difusas: Signos y patrones diagnósticos básicos. Medicina Respiratoria 5(3):49–67
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th international conference on neural information processing systems, vol 1, NIPS’15. MIT Press, Cambridge, MA, USA, pp 91–99
Girshick R (2015, December) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision (ICCV)
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: 2017 IEEE international conference on computer vision (ICCV)
Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: 2017 IEEE international conference on computer vision (ICCV)
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR)
Morozov SP, Andreychenko AE, Blokhin IA, Gelezhe PB, Gonchar AP, Nikolaev AE, Pavlov NA, Chernina VY, Gombolevskiy VA (2020) MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic. Digital Diagn 1(1):49–59
Pedrosa J, Aresta G, Ferreira C, Atwal G, Phoulady HA, Chen X, Chen R, Li J, Wang L, Galdran A, Bouchachia H, Kaluva KC, Vaidhya K, Chunduru A, Tarai S, Nadimpalli SPP, Vaidya S, Kim I, Rassadin A, Tian Z, Sun Z, Jia Y, Men X, Ramos I, Cunha A, Campilho A (2021) LNDb challenge on automatic lung cancer patient management. Med Image Anal 70
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Aguilar, E., La Cruz, A., Albertti, R., Carnier, M., Gavidia, L., Severeyn, E. (2023). Detection of Respiratory Disease Patterns Using Mask R-CNN. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-19-1610-6_65
Download citation
DOI: https://doi.org/10.1007/978-981-19-1610-6_65
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1609-0
Online ISBN: 978-981-19-1610-6
eBook Packages: EngineeringEngineering (R0)